Facial recognition authentication system including path parameters

ABSTRACT

Systems and methods for enrolling and authenticating a user in an authentication system via a user&#39;s camera of camera equipped mobile device include capturing and storing enrollment biometric information from at least one first image of the user taken via the camera of the mobile device, capturing authentication biometric information from at least one second image of the user, capturing, during imaging of the at least one second image, path parameters via at least one movement detecting sensor indicating an authentication movement of the mobile device, comparing the authentication biometric information to the stored enrollment biometric information, and comparing the authentication movement of the mobile device to an expected movement of the mobile device to determine whether the authentication movement sufficiently corresponds to the expected movement.

BACKGROUND 1. Field of the Invention

The disclosed embodiments relate to biometric security. More specifically, the disclosed embodiments relate to a facial recognition authentication systems.

2. Related Art

With the growth of personal electronic devices that may be used to access a number of user accounts, and the increasing threat of identity theft and other security issues, there is a growing need for ways to securely access user accounts via electronic devices. Account holders are thus often required to have longer passwords that meet various criteria such as using a mixture of capital and lowercase letters, numbers, and other symbols. With smaller electronic devices, such as smart phones, smart watches, “Internet of Things” (“IoT’) devices and the like, it may become cumbersome to attempt to type such long passwords into the device each time access to the account is desired. In some instances, users may even decide to deactivate such cumbersome security measures due to their inconvenience on their devices. Thus, users of such devices may prefer other methods of secure access to their user accounts.

One other such method may be through the use of biometrics. For example, an electronic device may have an optical reader that may scan a user's fingerprint to determine that the person requesting access to a device or an account is authorized. However, such fingerprint systems are often prohibitively expensive for use on a small electronic device or are often considered unreliable and unsecure.

In addition, facial recognition is generally known and may be used in a variety of contexts. Two-dimensional facial recognition is commonly used to tag people in images on social networks or in photo editing software. Facial recognition software, however, has not been widely implemented on its own to securely authenticate users attempting to gain access to an account because it not considered secure enough. For example, two-dimensional facial recognition is considered unsecure because faces may be photographed or recorded, and then the resulting prints or video displays showing images of the user may be used to trick the system. Accordingly, there is a need for reliable, cost-effective, and convenient method to authenticate users attempting to log in to, for example, a user account.

SUMMARY

The disclosed embodiments have been developed in light of the above and aspects of the invention may include a method for enrolling and authenticating a user in an authentication system via a user's a mobile computing device. The user's device includes a camera and at least one movement detecting sensor, such as an accelerometer, magnetometer, and gyroscope.

In one embodiment, the user may enroll in the system by providing enrollment images of the user's face. The enrollment images are taken by the camera of the mobile device as the user moves the mobile device to different positions relative to the user's head. The user may thus obtain enrollment images showing the user's face from different angles and distances. The system may also utilize one or more movement sensors of a mobile device to determine an enrollment movement path that the phone takes during the imaging. At least one image is processed to detect the user's face within the image, and to obtain biometric information from the user's face in the image. The image processing may be done on the user's mobile device or at a remote device, such as an authentication server or a user account server. The enrollment information (the enrollment biometrics, movement, and other information) may be stored on the mobile device or remote device.

The system may then authenticate a user by the user providing at least one authentication image via the camera of the mobile device while the user moves the mobile device to different positions relative to the user's head. The authentication images are processed for face detection and facial biometric information. Path parameters are also obtained during the imaging of the authentication images (authentication movement). The authentication information (authentication biometric, movement, and other information) is then compared with the enrollment information to determine whether the user should be authenticated or denied. Image processing and comparison may be conducted on the user's mobile device, or may be conducted remotely.

In some embodiments, multiple enrollment profiles may be created by a user to provide further security. For example, a user may create an enrollment wearing accessories such as a hat or glasses, or while making a funny face. In further embodiments, the user's enrollment information may be linked to a user email address, phone number, or other identifier.

The authentication system may include feedback displayed on the mobile device to aid a user in learning and authentication with the system. For instance, an accuracy meter may provide feedback on a match rate of the authentication biometrics or movement. A movement meter may provide feedback on the movement detected by the mobile device.

In some embodiments, the system may reward users who successfully utilize the authentication system or who otherwise take fraud preventing measures. Such rewards may include leaderboards, status levels, reward points, coupons or other offers, and the like. In some embodiments, the authentication system may be used to login to multiple accounts.

In addition to biometric and movement matching, some embodiments may also utilize banding detection, glare detection, and screen edge detection to further secure the system. In other embodiments, other user attributes may be detected and matched including users' gender, age, ethnicity, and the like.

The system may also provide gradual access to user account(s) when the user first sets up the authentication system. As the user successfully implements the system, authorization may be expanded. For example, during a time period as the user gets accustomed to the authentication system, lower transaction limits may be applied.

In some embodiments, the mobile device may show video feedback of what the user is imaging to aid the user to image his or her face during enrollment or authentication. The video feedback may be displayed on only a portion of the display screen of the mobile device. For example, the video feedback may be displayed in an upper portion of the display screen. The video feedback display may be position on a portion of the display screen that corresponds with a location of a front-facing camera of the mobile device.

To facilitate imaging in low-light, portions of the screen other than the video feedback may be displayed in a bright color, such as white. In some embodiments, and LED or infrared light may be used, and near infrared thermal imaging may be done with an infrared camera. The mobile device used for imaging may thus have multiple cameras for capture visible light and infrared images. The mobile device may also have multiple cameras (two or more) imaging in a single spectrum or multiple spectrum to provide stereoscopic, three-dimensional images. In such an embodiment, the close-up frames (zoomed) may create the most differentiation as compared to images captured from a distance. In such an embodiment, the frames captured at a distance may be unnecessary.

In some embodiments, to provide added security, the mobile device may output objects, colors, or patterns on the display screen to be detected during the imaging. The predetermined object or pattern may be a unique one-dimensional or two-dimensional barcode. For example, a QR code (two-dimensional barcode) may be displayed on the screen and reflected off of the user's eye. If the QR code is detected in the image, then the person may be authenticated. In other embodiments, an object may move on the screen and the system may detect whether a user's eyes follow the movement.

In some embodiments, the system may provide prompts on a video feedback display to aid the user in moving the device relative to the user's head during enrollment and/or authentication. The prompts may include ovals or frames displayed on the display screen in which the user must place his or her face by moving the mobile device until his or her face is within the oval or frame. The prompts may preferably be of differing sizes and may also be centered on different positions of the screen. When an actual, three-dimensional person images himself or herself close up and far away, it has been found that the biometric results are different due to the fish-eye effect of the lens. Thus, a three-dimensional person may be validated when biometric results are different in the close-up and far away images. This also allows the user to have multiple biometric profiles for each of the distances.

In other embodiments, biometrics from images obtained between the close-up and far away images may be analyzed for incrementally different biometric results. In this manner, the morphing of the face from the far face to the warped close up face is captured and tracked. The incremental frames during an authentication may then be matched to frames captured at similar locations during enrollment along the motion path and compared to ensure that the expected similarities and difference are found. This results in a motion path and captured image and biometric data that can prove a three-dimensional person is presently being imaged. Thus, not only are the close-up and far away biometrics compared, but also biometric data obtained in between. The biometric data obtained in between must also correspond to a correct morphing speed along the motion path, greatly enhancing the security of the system.

The touch screen may be utilized in some embodiments. For example, the user may need to enter a swipe a particular code or pattern in addition to the authentication system described herein. The touchscreen may also detect a size and orientation of a user's finger, and whether or not a right hand or a left hand is used on the touch screen. Voice parameters may also be used as an added layer of security. The system may detect edge sharpness or other indicators to ensure that the obtained images are of sufficient quality for the authentication system.

When a camera has an autofocus, the autofocus may be controlled by the system to validate the presence of the actual, three-dimensional person. The autofocus may check that different features of the user or environment focus at different focal lengths. In other embodiments, authentication images may be saved to review the person who attempted to authenticate with the system.

In some embodiments, the match thresholds required may be adapted over time. The system may thus account for changing biometrics due to age, weight gain/loss, environment, user experience, security level, or other factors. In further embodiments, the system may utilize image distortion prior to obtaining biometric information to further protect against fraudulent access.

The system may utilize any number or combination of the security features as security layers, as described herein. When authentication fails, the system may be configured so that it is unclear which security layer triggered the failure to preserve the integrity of the security system.

Other systems, methods, features and advantages of the invention will be or will become apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. In the figures, like reference numerals designate corresponding parts throughout the different views.

FIG. 1 illustrates an example environment of use of the facial recognition authentication system, according to one exemplary embodiment.

FIG. 2 illustrates an example embodiment of a mobile device.

FIG. 3 illustrates exemplary software modules that are part of the mobile device and server.

FIG. 4 shows a method for performing facial recognition authentication according to one embodiment.

FIG. 5 shows a method for enrolling a user in a facial recognition authentication system, according to one exemplary embodiment.

FIGS. 6A and 6B show an example of movement of a mobile device about a user's face according to one exemplary embodiment.

FIGS. 7A and 7B show an example of movement of a mobile device about a user's face according to one exemplary embodiment.

FIG. 8 shows a method of providing authentication information in a facial recognition authentication system, according to one exemplary embodiment.

FIG. 9 shows a method of verifying authentication credential in a facial recognition authentication system, according to one exemplary embodiment.

FIG. 10 illustrates an exemplary display showing a graphical and numeric feedback in a facial recognition authentication system.

FIGS. 11A, 11B, and 11C illustrate exemplary video feedback displays corresponding to front-facing camera positions in a facial recognition authentication system.

FIG. 12 shows an exemplary video display feedback of a facial recognition authentication system where edge pixels on the sides of the display are stretched horizontally.

FIGS. 13A and 13B illustrate exemplary screen displays with face alignment indicators shown as an oval to serve as a guide as the user moves the mobile device closer to or away from their face.

FIG. 14 illustrates an exemplary mobile device display showing a graphical code entry interface with an imaging area.

FIG. 15 illustrates an example mobile device display showing a numeric and graphical code entry interface with an imaging area.

DETAILED DESCRIPTION OF EMBODIMENTS

A system and method for providing secure and convenient facial recognition authentication will be described below. The system and method may be achieved without the need for additional expensive biometric readers or systems while offering enhanced security over conventional facial recognition systems.

Facial Recognition Authentication Environment

FIG. 1 illustrates an example environment of use of the facial recognition authentication system described herein. This is but one possible environment of use and system. It is contemplated that, after reading the specification provided below in connection with the figures, one of ordinary skill in the art may arrive at different environments of use and configurations.

In this environment, a user 108 may have a mobile device 112 which may be used to access one or more of the user's accounts via authentication systems. A user 108 may have a mobile device 112 that is capable of capturing a picture of the user 108, such as an image of the user's face. The user may use a camera 114 on or connected to the mobile device 112 to capture an image or multiple images or video of himself or herself. The mobile device 112 may comprise any type of mobile device capable of capturing an image, either still or video, and performing processing of the image or communication over a network.

In this embodiment, the user 108 may carry and hold the mobile device 112 to capture the image. The user may also wear or hold any number of other devices. For, example, the user may wear a watch 130 containing one or more cameras 134 or biosensors disposed on the watch. The camera 134 may be configured to create an image from visible light as well as infrared light. The camera 134 may additionally or alternatively employ image intensification, active illumination, or thermal vision to obtain images in dark environments.

When pointed towards a user 108, the camera 134 may capture an image of the user's face. The camera 134 may be part of a module that may either include communication capability that communicates with either a mobile device 112, such as via Bluetooth®, NFC, or other format, or communication directly with a network 116 over a wired or wireless link 154. The watch 130 may include a screen on its face to allow the user to view information. If the camera module 134 communicates with the mobile device 112, the mobile device 134 may relay communications to the network 116. The mobile device 134 may be configured with more than one front facing camera 114 to provide for a 3D or stereoscopic view, or to obtain images across a different spectral ranges, such as near infrared and visible light.

The mobile device 112 is configured to wirelessly communicate over a network 116 with a remote server 120. The server 120 may communicate with one or more databases 124. The network 116 may be any type of network capable of communicating to and from the mobile device including but not limited to a LAN, WAN, PAN, or the Internet. The mobile device 112 may communicate with the network via a wired or wireless connection, such as via Ethernet, WiFi, NFC, and the like. The server 120 may include any type of computing device capable of communicating with the mobile device 112. The server 120 and mobile device 112 are configured with a processor and memory and are configured to execute machine readable code or machine instructions stored in the memory.

The database 124, stored on mobile device or remote location as shown, may contain facial biometric information and authentication information of users 108 to identify the users 108 to allow access to associated user data based on one or more images or biometric information received from the mobile device 112 or watch 134. The data may be, for example, information relating to a user account or instruction to allow access to a separate account information server 120B. The term biometric data may include among other information biometric information concerning facial features and path parameters. Examples of path parameters may include an acceleration and speed of the mobile device, angle of the mobile device during image capture, distance of the mobile device to the user, path direction in relation to the user's face position in relation to the user, or any other type parameter associated with movement of the mobile device or the user face in relation to a camera. Other data may also be included such as GPS data, device identification information, and the like.

In this embodiment, the server 120 processes requests for identification from the mobile device 112 or user 108. In one configuration, the image captured by the mobile device 112, using facial detection, comprises one or more images of the user's face 108 during movement of the mobile device relative to the user's face, such as in a side to side or horizontal arc or line, vertical arc or line, forward and backwards from the user's face, or any other direction of motion. In another configuration, the mobile device 112 calculates biometric information from the obtained images, and sends the biometric information to the server 120. In yet another embodiment, the mobile device 112 compares biometric information with stored biometric information on the mobile device 112, and sends a authentication result from the comparison to the server 120.

The data including either the image(s), biometric information, or both are sent over the network 116 to the server 120. Using image processing and image recognition algorithms, the server 120 processes the person's biometric information, such as facial data, and compares the biometric information with biometric data stored in the database 124 to determine the likelihood of a match. In other embodiments, the image processing and comparison is done on the mobile device 112, and data sent to the server indicates a result of the comparison. In further embodiments, the image processing and comparison is done on the mobile device 112 without accessing the server, for example, to obtain access to the mobile device 112 itself.

By using facial recognition processing, an accurate identity match may be established. Based on this and optionally one or more other factors, access may be granted or an unauthorized user may be rejected. Facial recognition processing is known in the art (or is an established process) and as a result, it is not described in detail herein.

Also shown is a second server 120B with associated second database 124B, and third server 120C with associated third database 124C. The second and third database may be provided to contain additional information that is not available on the server 120 and database 124. For example, one of the additional servers may only be accessed based on the authentication of the user 108 performed by the server 120.

Executing on the mobile device 112 is one or more software applications. This software is defined herein as an identification application (ID App). The ID App may be configured with either or both of facial detection and facial recognition and one or more software modules which monitor the path parameters and/or biometric data. Facial detection as used herein refers to a process which detects a face in an image. Facial recognition as used herein refers to a process that is capable of analyzing a face using an algorithm, mapping its facial features, and converting them to biometric data, such as numeric data. The biometric data can be compared to that derived from one or more different images for similarities or dis-similarities. If a high percentage of similarity is found in the biometric data, the individual shown in the images may be considered to be a match.

With the ultimate goal of matching a face of a user to an identity or image stored in a database 124, to authenticate the user, the ID App may first process the image captured by the camera 114, 134 to identify and locate the face that is in the image. As shown in FIG. 1 , there may be the face 108. The authentication may be used for logging into an online account or for numerous other access control functions.

The portion of the photo that contains the detected face may then be cropped, cut, and stored for processing by one or more facial recognition algorithms. By first detecting the face in the image and cropping only that portion of the face, the facial recognition algorithm need not process the entire image. Further, in embodiments where the facial recognition processing occurs remotely from the mobile device 112, such as at a server 120, much less image data is required to be sent over the network to the remote location. It is contemplated that the entire image, a cropped face, or only biometric data may be sent to the remote server 120 for processing.

Facial detection software is capable of detecting a face from a variety of angles. However, facial recognition algorithms are most accurate in straight on images in well-lit situations. In one embodiment, the highest quality face image for facial recognition that is captured is processed first, then images of the face that are lower quality or at different angles other than straight toward the face are then processed. The processing may occur on the mobile device or at a remote server which has access to large databases of image data or facial identification data.

The facial detection is preferred to occur on the mobile device and is performed by the mobile device software, such as the ID App. This reduces the number or size of images (data) that are sent to the server for processing where faces are not found and minimizes the overall amount of data that must be sent over the network. This reduces bandwidth needs and network speed requirements are reduced.

In another preferred embodiment, the facial detection, facial recognition, and biometric comparison all occur on the mobile device. However, it is contemplated that the facial recognition processing may occur on the mobile device, the remote server, or both.

FIG. 2 illustrates an example embodiment of a mobile device. This is but one possible mobile device configuration and as such it is contemplated that one of ordinary skill in the art may differently configure the mobile device. The mobile device 200 may comprise any type of mobile communication device capable of performing as described below. The mobile device may comprise a PDA, cellular telephone, smart phone, tablet PC, wireless electronic pad, an IoT device, a “wearable” electronic device or any other computing device.

In this example embodiment, the mobile device 200 is configured with an outer housing 204 configured to protect and contain the components described below. Within the housing 204 is a processor 208 and a first and second bus 212A, 212B (collectively 212). The processor 208 communicates over the buses 212 with the other components of the mobile device 200. The processor 208 may comprise any type processor or controller capable of performing as described herein. The processor 208 may comprise a general purpose processor, ASIC, ARM, DSP, controller, or any other type processing device. The processor 208 and other elements of the mobile device 200 receive power from a battery 220 or other power source. An electrical interface 224 provides one or more electrical ports to electrically interface with the mobile device, such as with a second electronic device, computer, a medical device, or a power supply/charging device. The interface 224 may comprise any type electrical interface or connector format.

One or more memories 210 are part of the mobile device 200 for storage of machine readable code for execution on the processor 208 and for storage of data, such as image data, audio data, user data, medical data, location data, accelerometer data, or any other type of data. The memory 210 may comprise RAM, ROM, flash memory, optical memory, or micro-drive memory. The machine readable code as described herein is non-transitory.

As part of this embodiment, the processor 208 connects to a user interface 216. The user interface 216 may comprise any system or device configured to accept user input to control the mobile device. The user interface 216 may comprise one or more of the following: keyboard, roller ball, buttons, wheels, pointer key, touch pad, and touch screen. A touch screen controller 230 is also provided which interfaces through the bus 212 and connects to a display 228.

The display comprises any type display screen configured to display visual information to the user. The screen may comprise a LED, LCD, thin film transistor screen, OEL CSTN (color super twisted nematic), TFT (thin film transistor), TFD (thin film diode), OLED (organic light-emitting diode), AMOLED display (active-matrix organic light-emitting diode), capacitive touch screen, resistive touch screen or any combination of these technologies. The display 228 receives signals from the processor 208 and these signals are translated by the display into text and images as is understood in the art. The display 228 may further comprise a display processor (not shown) or controller that interfaces with the processor 208. The touch screen controller 230 may comprise a module configured to receive signals from a touch screen which is overlaid on the display 228.

Also part of this exemplary mobile device is a speaker 234 and microphone 238. The speaker 234 and microphone 238 may be controlled by the processor 208. The microphone 238 is configured to receive and convert audio signals to electrical signals based on processor 208 control. Likewise, the processor 208 may activate the speaker 234 to generate audio signals. These devices operate as is understood in the art and as such are not described in detail herein.

Also connected to one or more of the buses 212 is a first wireless transceiver 240 and a second wireless transceiver 244, each of which connect to respective antennas 248, 252. The first and second transceiver 240, 244 are configured to receive incoming signals from a remote transmitter and perform analog front end processing on the signals to generate analog baseband signals. The incoming signal may be further processed by conversion to a digital format, such as by an analog to digital converter, for subsequent processing by the processor 208. Likewise, the first and second transceiver 240, 244 are configured to receive outgoing signals from the processor 208, or another component of the mobile device 208, and up convert these signal from baseband to RF frequency for transmission over the respective antenna 248, 252. Although shown with a first wireless transceiver 240 and a second wireless transceiver 244, it is contemplated that the mobile device 200 may have only one such system or two or more transceivers. For example, some devices are tri-band or quad-band capable, or have Bluetooth®, NFC, or other communication capability.

It is contemplated that the mobile device, and hence the first wireless transceiver 240 and a second wireless transceiver 244 may be configured to operate according to any presently existing or future developed wireless standard including, but not limited to, Bluetooth, WI-FI such as IEEE 802.11 a,b,g,n, wireless LAN, WMAN, broadband fixed access, WiMAX, any cellular technology including CDMA, GSM, EDGE, 3G, 4G, 5G, TDMA, AMPS, FRS, GMRS, citizen band radio, VHF, AM, FM, and wireless USB.

Also part of the mobile device is one or more systems connected to the second bus 212B which also interface with the processor 208. These devices include a global positioning system (GPS) module 260 with associated antenna 262. The GPS module 260 is capable of receiving and processing signals from satellites or other transponders to generate location data regarding the location, direction of travel, and speed of the GPS module 260. GPS is generally understood in the art and hence not described in detail herein. A gyroscope 264 connects to the bus 212B to generate and provide orientation data regarding the orientation of the mobile device 204. A magnetometer 268 is provided to provide directional information to the mobile device 204. An accelerometer 272 connects to the bus 212B to provide information or data regarding shocks or forces experienced by the mobile device. In one configuration, the accelerometer 272 and gyroscope 264 generate and provide data to the processor 208 to indicate a movement path and orientation of the mobile device.

One or more cameras (still, video, or both) 276 are provided to capture image data for storage in the memory 210 and/or for possible transmission over a wireless or wired link or for viewing at a later time. The one or more cameras 276 may be configured to detect an image using visible light and/or near-infrared light. The cameras 276 may also be configured to utilize image intensification, active illumination, or thermal vision to obtain images in dark environments. The processor 208 may process image data to perform image recognition, such as in the case of, facial detection, item detection, facial recognition, item recognition, or bar/box code reading.

A flasher and/or flashlight 280, such as an LED light, are provided and are processor controllable. The flasher or flashlight 280 may serve as a strobe or traditional flashlight. The flasher or flashlight 280 may also be configured to emit near-infrared light. A power management module 284 interfaces with or monitors the battery 220 to manage power consumption, control battery charging, and provide supply voltages to the various devices which may require different power requirements.

FIG. 3 illustrates exemplary software modules that are part of the mobile device and server. Other software modules may be provided to provide the functionality described below. It is provided that for the functionality described herein there is matching software (non-transitory machine readable code, machine executable instructions or code) configured to execute the functionality. The software would be stored on a memory and executable by a processor.

In this example confirmation, the mobile device 304 includes a receive module 320 and a transmit module 322. These software modules are configured to receive and transmit data to remote device, such as cameras, glasses, servers, cellular towers, or WIFI system, such as router or access points.

Also part of the mobile device 304 is a location detection module 324 configured to determine the location of the mobile device, such as with triangulation or GPS. An account setting module 326 is provided to establish, store, and allow a user to adjust account settings. A log in module 328 is also provided to allow a user to log in, such as with password protection, to the mobile device 304. A facial detection module 308 is provided to execute facial detection algorithms while a facial recognition module 321 includes software code that recognizes the face or facial features of a user, such as to create numeric values which represent one or more facial features (facial biometric information) that are unique to the user.

An information display module 314 controls the display of information to the user of the mobile device. The display may occur on the screen of the mobile device or watch. A user input/output module 316 is configured to accept data from and display data to the user. A local interface 318 is configured to interface with other local devices, such as using Bluetooth® or other shorter range communication, or wired links using connectors to connected cameras, batteries, data storage elements. All of the software (with associated hardware) shown in the mobile device 304 operate to provide the functionality described herein.

Also shown in FIG. 3 is the server software module 350. These modules are located remotely from the mobile device, but can be located on any server or remote processing element. As is understood in the art, networks and network data use a distributed processing approach with multiple servers and databases operating together to provide a unified server. As a result, it is contemplated that the module shown in the server block 350 may not all be located at the same server or at the same physical location.

As shown in FIG. 3 , the server 350 includes a receive module 352 and a transmit module 354. These software modules are configured to receive and transmit data to remote devices, such as cameras, watches, glasses, servers, cellular towers, or WIFI systems, such as router or access points.

An information display module 356 controls a display of information at the server 350. A user input/output module 358 controls a user interface in connection with the local interface module 360. Also located on the server side of the system is a facial recognition module 366 that is configured to process the image data from the mobile device. The facial recognition module 366 may process the image data to generate facial data (biometric information) and perform a compare function in relation to other facial data to determine a facial match as part of an identify determination.

A database interface 368 enables communication with one or more databases that contain information used by the server modules. A location detection module 370 may utilize the location data from the mobile device 304 for processing and to increase accuracy. Likewise an account settings module 372 controls user accounts and may interface with the account settings module 326 of the mobile device 304. A secondary server interface 374 is provided to interface and communicate with one or more other servers.

One or more databases or database interfaces are provided to facilitate communication with and searching of databases. In this example embodiment the system includes an image database that contains images or image data for one or more people. This database interface 362 may be used to access image data users as part of the identity match process. Also part of this embodiment is a personal data database interface 376 and privacy settings data module 364. These two modules 376, 364 operate to establish privacy setting for individuals and to access a database that may contain privacy settings.

Authentication System

An authentication system with path parameters that is operable in the above described environment and system will now be described in connection with FIG. 4 . FIG. 4 shows a method for performing facial recognition authentication with path parameters according to one embodiment of the invention. As will be described in more detail below, the system utilizes the features of the mobile device 112 and server 120 defined above to generate a secure and convenient login system as one example of an authentication system. This reduces the burden of the user having to type in complex passwords onto a small screen of a mobile device, prevents fraud through means such as key logging or screen shot captures, and increases security by combining several path parameters and/or device parameters which must be met before user is authenticated.

In step 410, the system enrolls a user in the facial recognition authentication system. In one embodiment, an authentication server, such as the server 120 (FIG. 1 ), may be configured to authenticate a user to allow access to a user's account, such as a bank or other account, via the mobile device 112. The authentication server 120 may be included as a part of a server of the institution or entity providing user accounts (hereinafter “account server”), or the authentication server may be provided separately. For example, in the environment shown in FIG. 1 , Servers 120B and 120C may represent account servers. In other embodiments, the account server and the authentication server are one in the same. In one embodiment, the authentication server 120 may provide an authentication application to the user for installation on the mobile device 112.

An enrollment process according to one embodiment will be described with reference to FIG. 5 . In this embodiment, a user via a mobile device 112 establishes a connection between the mobile device 112 and the account server 120B in step 510. As just one example, the user may establish a connection with a server of a financial institution such as a bank, or this connection may occur later in the process after authentication. The user then provides typical login information to authenticate the user, such as a user name and password for a financial account in step 512. In step 514, the user may next receive a prompt at the mobile device 112 to enroll in the facial recognition authentication system. The user then, via the user interface, indicates that he or she would like to set up the authentication system in response to the prompt.

Next, in step 516, the mobile device 112 may send device information to the authentication server 120. The device information may include among other information a device identifier that uniquely identifies the mobile device of the user. Such information may include device manufacturer, model number, serial number, and mobile network information. In step 518, when the authentication server 120 is incorporated with the account server 120B, the authentication server 120 associates and stores the device information with the user's account information. When the authentication server 120 is separate from the account server 120B, the account server 120B may generate a unique identifier related to the account information and send the unique identifier to the authentication server 120. The authentication server 120 may associate the device information and the unique identifier with each other and may store the information in a database 124.

The user is next prompted to provide a plurality of images of his or her face using a camera 114 on the mobile device 112 (hereinafter, “enrollment images”) in step 510. The enrollment images of the user's face are taken as the user holds the mobile device and moves the mobile device to different positions relative to his or her head and face. Thus, the enrollment images of the user's face are taken from many different angles or positions. Furthermore, the path parameters of the mobile device are monitored and recorded for future comparison in step 522. Some non-limiting examples of how a user might hold a mobile device and take a plurality of images of her face is shown in FIGS. 6A-7B.

In FIGS. 6A and 6B, the user holds the mobile device 112 on one side of his or her face, and moves the mobile device 112 in an arc like path horizontally about his or her face until the mobile device 112 is on the other side of her or her face. In FIGS. 7A and 7B, the user holds the mobile device 112 far away from his or her face, and then brings the mobile device 112 forward closer to his or her face. Of course, any number of other paths may be used in addition to those shown in FIGS. 6A-7B. Additionally, the user may move his or her head while the camera is held fixed. The user could also hold the camera steady and move their head in relation to the camera. This method thus can be implemented with a webcam on a laptop or desktop, or on any other device, such as an IoT device where a camera is mounted on a similarly stationary location or object.

The enrollment images may be obtained as follows. The user holds and orients a mobile device 112 with a camera 114 so that the camera 114 is positioned to image the user's face. For example, the user may use a front facing camera 114 on a mobile device 112 with a display screen and may confirm on the display screen that his or her face is in position to be imaged by the camera 114.

Once the user has oriented the device, the device may begin obtaining the enrollment images of the user. In one embodiment, the user may press a button on the device 112 such as on a touchscreen or other button on the device to initiate the obtaining of the enrollment images. The user then moves the mobile device to different positions relative to his or her head as the device images the user's face from a plurality of angles or positions as described above. When the above-mentioned front-facing camera is used, the user may continually confirm that his or her face is being imaged by viewing the imaging on the display screen. The user may again press the button to indicate that the imaging is completed. Alternatively the user may hold the button during imaging, and then release the button to indicate that imaging is complete.

As described above, the mobile device 112 may include face detection. In this embodiment in step 524, the mobile device may detect the user's face in each of the enrollment images, crop the images to include only the user's face, and send, via a network, the images to the authentication server 120. In step 526, upon receipt of the enrollment images, the authentication server 120 performs facial recognition on the images to determine biometric information (“enrollment biometrics”) for the user. The authentication server 120 may then associate the enrollment biometrics with the device information and the unique identifier (or account information) and stores the biometric information in the database 124 in step 528. For added security, in step 530, the mobile device 112 and the authentication server 120 may be configured to delete the enrollment images after the enrollment biometrics of the user are obtained.

In another embodiment, the mobile device 112 may send the images to the authentication server 120 without performing face detection. The authentication server 120 may then perform the face detection, facial recognition, and biometric information processing. In another embodiment, the mobile device 112 may be configured to perform the facial detection, facial recognition, and biometric processing, and then send the results or data resulting from the processing to the authentication server 120 to be associated with the unique identifier or user account. This prevents sensitive personal data (images) from leaving the user's device. In yet another embodiment, the mobile device 112 may perform each of the above mentioned steps, and the mobile device 112 may store the enrollment information without sending any of the enrollment biometrics or images to the server.

In one embodiment, the mobile device's gyroscope, magnetometer, and accelerometer are configured to generate and store data while the user moves the mobile device about his or her head to obtain the enrollment images (path parameters). The mobile device may process this data in step 532 to determine a path or arc in which the mobile device moved while the user imaged his or her face (“enrollment movement”). By using data from the accelerometer, magnetometer, and gyroscope, the system may check when a user is ready to begin scanning himself/herself, as well as determining the scan path. The data is thus used to determine when to start and stop the scan interval. The data may additionally include the time elapsed during scanning. This time may be measured from the user pressing the button to start and stop the imaging, or may be measured from the duration the button is held down while imaging, or during more movement or to complete sweep.

The enrollment movement of the mobile device 112 (which is data that defined the movement of the mobile device during image capture) may be sent to the authentication server 120. The authentication server 120 associates and stores the enrollment movement, the enrollment biometrics, the device information, and the unique identifier or account information. Alternatively, the data generated by the gyroscope, magnetometer, and accelerometer may be sent to the server 120, and the server 120 may process the data to determine the enrollment movement.

Thus, in the above described embodiment, the enrollment information may thus comprise the device information, the enrollment biometrics, and the enrollment movement (based on movement of the mobile device 112).

Returning to FIG. 4 , once enrollment is complete, the authentication server 120 may later receive credentials from a user attempting to authenticate with the system as shown in step 420. For example, a user may attempt to log in to a user account. When a user attempts to log in, instead of or in addition to providing typical account credentials such as user name and password, the user may again take a plurality of images or video of his or her face as the mobile device 112 is held in the hand and moved to different positions relative to the head (“authentication images”) in the same manner as was done during enrollment (such as shown in FIGS. 6A-7B). In this manner, the user may provide the necessary images (the term images includes video as video is a succession of images) from many different angles and/or positions, and may provide path parameters of the device while obtaining the images (“authentication movement”) to both confirm the identity of the user as well as the liveness and realness of that individual to ensure it is not a video, screen shot, or other representation of the person.

In one embodiment outlined in FIG. 8 , the user via the mobile device 112 obtains a number of authentication images in step 810 while moving the mobile device 112 to different positions relative to the user's head. Using facial detection in step 812, the mobile device 112 detects the user's face in each of the authentication images, crops the images, and sends the images to the authentication server 120. In another embodiment, the mobile device 112 sends the images to the server 124, and the server 124 performs facial detection. In step 814, the authentication routing 120 may perform facial recognition on the authentication images to obtain biometric information (“authentication biometrics”). In another embodiment, the mobile device 112 performs facial recognition to obtain the authentication biometrics and sends the authentication biometrics to the server 120.

In step 816, the mobile device 112 sends the device information identifying the device and sends path parameters such as gyroscope, magnetometer, and accelerometer information defining the path of the mobile device taken during imaging, as well as the elapsed time during imaging (“authentication movement”) to the server 120. The credentials received by the authentication server 120 for a login in the facial recognition system may thus comprise the device information, the authentication images or the authentication biometrics, and the authentication movement (path parameters).

Returning to FIG. 4 , in step 430, the authentication server 120 verifies that the credentials received from the mobile device 112 sufficiently correspond with the information obtained during enrollment. For example, as shown in step 910 in FIG. 9 , by using algorithms to process the characteristics of the face and light striking the face between the different images, the authentication server 120 can determine that the face in the authentication images is three-dimensional, i.e. not a representation on a printed picture or video screen. Where the mobile device 120 sends only the authentication biometrics 120 to the server, the server 120 may validate the realness or three-dimensional aspects of the user imaged by comparing the biometric results of the different images.

In step 920, the authentication server 120 may then compare the login credentials with the information stored from the enrollment process. In step 920, the server 120 compares the identification of the device obtained during the login process to that stored during enrollment. In step 930, the authentication biometrics may be compared with the enrollment biometrics to determine whether they sufficiently correspond with the enrollment biometrics. In step 940, the authentication movement may be compared with the enrollment movement to determine whether it sufficiently corresponds with the enrollment movement.

In some embodiments, a copy of the enrollment information may be stored on the mobile device 112, and the mobile device 112 may verify that the credentials received on the mobile device 112 sufficiently correspond with the enrollment information. This would allow a user to secure documents, files, or applications on the mobile device 112 itself in addition to securing a user's account hosted on a remote device, such as the authentication server 120, even when a connection to the authentication server 120 may be temporarily unavailable, such as when a user does not have access to the Internet. Further, this would allow the user to secure access to the mobile device 112 itself. Or enrollment info may be stored on server.

Accordingly, in step 950, if the authentication server 120 or mobile device 112 determines that the enrollment information sufficiently corresponds with the credentials received, then the server or mobile device may verify that the identification of the user attempting login corresponds the account holder. This avoids the cumbersome process of the user having to manually type in a complex password using the small screen of the mobile device. Many passwords now require capital, non-text letter, lower case, and numbers.

The level of correspondence required to determine that the enrollment information sufficiently corresponds with the authentication information in the login attempt may be set in advance. For example, the level of correspondence may be a 99.9% match rate between the enrollment biometrics and the authentication biometrics and a 90% match rate between the enrollment movement and the authentication movement. The required level of correspondence may be static or elastic based on the established thresholds.

For example, the required level of correspondence may be based on GPS information from the mobile device 112. In one embodiment, the authentication server 120 may require a 99.9% match rate as the level of correspondence when the GPS information of the mobile device corresponds with the location of the user's home or other authorized location(s). In contrast, if the GPS information shows the device is in a foreign country far from the user's home, the authentication server may require a 99.99% match rate as the level of correspondence or may be denied entirely. Hence, the required match between pre-stored authentication data (enrollment information) and presently received authentication data (authentication information) is elastic in that the required percentage match between path parameters or images my change depending on various factors, such as time of day, location, frequency of login attempt, date, or any other factor.

The required level of correspondence may additionally depend on time. For instance, if a second authentication attempt is made shortly after a first authentication attempt in a location far from the first authentication location based on GPS information from the mobile device 112, the level of correspondence threshold may be set higher. For example, a user can not travel from Seattle to New York in 1 hour. Likewise, login attempts at midnight to three in the morning may be a sign of fraud for some users based on patterns of the users' usage.

The level of correspondence between the enrollment information and the authentication information may be the result of compounding the various parameters of the enrollment information and the authentication information. For example, when the button hold time in the authentication information is within 5% of the button hold time of the enrollment information, the correspondence of the button hold time may constitute 20% of the overall match. Similarly when the motion path trajectory of the authentication information is within 10% of the enrollment information, the motion path trajectory may constitute 20% of the overall match. Further parameter match rates such as the face size and facial recognition match in the authentication information as compared to the enrollment information may constitute the remaining 10% and 50% of the overall level of correspondence. In this manner, the total overall level of correspondence may be adjusted (total of all parameters being more than 75%, for example), or the match rate of individual parameters may be adjusted. For example, on a second attempted login, the threshold match rate of one parameter may be increased, or the overall level of correspondence for all parameters may be increased. The threshold match rates may also be adjusted based on the account being authenticated or other different desired levels of security.

Returning to FIG. 4 , in step 440, the authentication server 120 may grant or deny access based on the verification in step 430. For example, if the authentication server 120 verifies that the credentials match the enrollment information, then the server 120 may authenticate the user to allow access to the user's account. In the instance where the authentication server 120 is separate from the account server 120B (such as a bank's server), the authentication server 120 may transmit the unique identifier to the account server along with an indication that the identity of the user associated with the unique identifier has been verified. The account server 120B may then authorize the user's mobile device 112 to transmit and receive data from the account server 120B. Of course, all this may occur at only the account server 120B or on the mobile device 112 itself.

Alternatively, if the credentials provided by the user are not verified, the authentication server may transmit a message to display on the screen of the mobile device 112 indicating that the login attempt failed. The authentication server 120 may then allow the user to try again to log in via the facial recognition login system, or the authentication server 120 may require the user to enter typical account credentials, such as a user name and password.

In one embodiment, the server 120 may allow three consecutive failed login attempts before requiring a user name and password. If in one of the attempts, the required level of correspondence is met, then the user may be verified and access may be granted. According to one embodiment, the authentication server 120 may retain the information from each successive authentication attempt and combine the data from the multiple authentication attempts to achieve more accurate facial biometric information of the person attempting to authenticate. In addition, the level of correspondence may be increased at each successive attempt to authenticate. In addition, by averaging the path data (authentication movement) and/or image data (authentication images/biometrics) from several login attempts, the login data (enrollment information) is perfected and improved.

Accordingly, the above described authentication system allows for authentication to a remote server 120 or on the mobile device 112 itself. This may be accomplished as described above by the mobile device 112 capturing the authentication credentials, and the authentication server 120 processing and analyzing the credentials compared to the enrollment information (cloud processing and analysis); the mobile device 112 capturing the authentication credentials and processing the credentials, and the authentication server 120 analyzing the credentials compared to the enrollment information (mobile device processing, cloud analysis); or the mobile device 112 capturing the authentication credentials, and processing and analyzing the credentials compared to the enrollment information (mobile device processing and analysis).

Advantages and Features of the Embodiments

The above described system provides a number of advantages. As one advantage, the facial recognition authentication system provides a secure login. For example, if during a login attempt the camera of the mobile device imaged a digital screen displaying a person rotating their head while the phone was not moving, the accelerometer, magnetometer, and gyroscope data would not detect any motion. Thus, the enrollment movement and the authentication movement would not correspond, and the login attempt would be denied.

In addition, because a plurality of images are used as enrollment images and authentication images, histograms or other photo manipulation techniques may be used to determine if a digital screen is present in place of a human face in the images. For example, the system may check for light frequency changes in the captured images, or banding in an image which would indicate an electronic display generated the image, backlighting, suspicious changes in lighting, or conduct other analyses on the images by comparing the images to determine that the actual live user is indeed alive, present, and requesting authorization to login.

As yet another advantage, as explained above, not only must the enrollment biometrics sufficiently correspond to the authentication biometrics, but also the enrollment movement must match the authentication movement, and the device information must match the enrollment device information. For example, an application may be downloaded to a mobile device that has a digital camera. The application may be a login application, or may be an application from a financial institution or other entity with which the user has an account. The user may then login to the application using typical login credential such as a website user name and password. Further, the user may have a device code from logging in on another device, or may use the camera to scan QR code or other such code to pair the device to their user account.

The user then holds the mobile device to move the mobile phone to different positions relative to his or her head while keeping his or her face visible to the camera as it is moved. As the mobile device is moved, the camera takes the enrollment images of the face. During imaging, the speed and angle of the current user's mobile device movement is measured using the accelerometer, magnetometer, and gyroscope to generate the enrollment movement. Further continuous imaging and detection of the face throughout the process has been shown to prevent fraud. This is because a fraud attempt cannot be made by rotating images in and out of the front of the camera.

For example, a user may start the movement from right to left or from left to right as shown in FIGS. 6A and 6B. The movement may also be in a front and back direction as shown in FIGS. 7A and 7B. Any other movement may be utilized such as starting in the center, then going right, and then going back to center. Vertical and diagonal movements may also be used to further compound the complexity of the enrollment movement. When the user then later attempts login, the user must repeat the motion pattern in the authentication movement so as to match the enrollment movement in addition to the biometric data and device information matching. Thus, the security of the system is greatly enhanced.

The system therefore provides enhanced security for authenticating a user who has a mobile device. As explained above, the system may use at least any one or more of the following in any number of combinations to securely authenticate the user: physical device verification, mobile network verification, facial recognition including the size of the face in the image, a face detected in every frame during the movement, accelerometer information, gyroscope information, magnetometer information, pixels per square inch, color bits per pixel, type of image, user entered code or pattern, and GPS information.

As another advantage, the facial recognition login system provides a convenient manner for a user to login to an account with a mobile device. For example, once enrolled, a user does not need to enter a user name and password on the small mobile device each time the user wishes to access the account. Instead, the user simply needs to image himself or herself while mimicking the enrollment movement with the mobile device. This is especially advantageous with smaller mobile devices such as mobile phones, smart watches, and the like.

The system may be further configured to allow a user to securely log on to multiple devices, or to allow users to securely share devices. In one embodiment, the enrollment information may be stored on an authentication server (or on “the cloud”) and thus is not associated only with the user's original device. This allows the user to use any number of suitable devices to authenticate with the authentication server. In this manner, a user may use a friend's phone (third party device) or other device to access his or her information, such as account information, address book information, email or other messaging, etc. by performing the authentication operation on any device.

For example, the user may provide an email address, user name code, or similar identifier on the friend's phone such that the authentication server compares the login information with enrollment information for the user's account. This would indicate to the authentication server which authentication profile to use, but does not by itself allow access to the user's data, accounts, or tasks. Upon logging out of a friend's phone, access to the user's information on the friend's phone is terminated. The provides the benefit of allowing a user to securely access account or other authentication accessible information or tasks using any device without having to type the user's password into the third party device, where it could be logged or copied. In a sense, the user is the password.

Through cloud-based enrollment information, a single user may also securely transfer data between authenticated devices. In one embodiment, a user may own a first device, such as a mobile phone, and is authenticated on the first device via the authentication system. The user may then acquire a new device, such as a new phone, tablet computer, or other device. Using the cloud-based authentication system, the user may authenticate on the new device and transfer data from the first device to the new device. The transfer of data may be completed via the Internet, a local network connection, a Bluetooth connection, a wired connection, or a near field communication. The authentication process may also be part of a security check to resent or restore a system after the phone is lost or stolen. Thus, the authentication system may be used to activate or authenticate a new device, with the authentication used to verify the user of the new device.

Similarly, the system may facilitate secure access to a single shared device by multiple people to control content or other features on the device. In many cases, passwords can be viewed, copied, guessed, or otherwise detected, particularly when a device is shared by a number of users. The users may be, for example, family members including parents and children, coworkers, or other relationships, such as students. The authentication system may allow each of the family members to log in based on his or her own unique enrollment information associated with a user account.

The device may restrict access to certain content or features for one or more of the certain user's accounts, such as children's user accounts, while allowing access to content and features for others, such as the parents' accounts. By using the authentication system for the shared device, the users such as children are unable to utilize a password to try and gain access to the restricted content because the authentication system requires the presence of the parent for authentication, as explained above. Thus device sharing among users with different privileges is further secured and enhanced. Likewise, in a classroom setting, a single device may be securely shared between multiple people for testing, research, and grade reporting.

Adaptations and Modifications

Numerous modifications may be made to the above system and method without departing from the scope of the invention. For example, the images may be processed by a facial recognition algorithm on the device and may also be converted to biometric data on the device which is then compared to previously created biometric data for an authorized user. Alternatively, the images from a device may be sent through a wired or wireless network where the facial recognition algorithms running on a separate server can process the images, create biometric data and compare that data against previously stored data that assigned to that device.

Multiple Profiles for a Single User

Further, the photo enrollment process may be done multiple times for a user to create multiple user profiles. For example, the user may enroll with profiles with and without glasses on, with and without other wearable devices, in different lighting conditions, wearing hats, with different hair styles, with or without facial or ear jewelry, or making different and unique faces, such as eyes closed, winking or tongue out to establish another level of uniqueness to each user profile. Such ‘faces’ made by the user would not be available on the user's Social Media Pages and hence not available for copying, manipulation, and use during a fraud attempt. Each set of enrollment images, enrollment biometrics, or both may be saved along with separate enrollment movement. In one embodiment at least three images are captured as the mobile device completes the path. It is contemplated that any number of images may be captured.

Linking Enrollment Information

It is also contemplated that the enrollment process may be linked to an email address, phone number, or other identifier. For example, a user may sign up with an email address, complete one or more enrollments as described above, and confirm the enrollments via the same email address. The email address may then further enhance the security of the system. For example, if a user unsuccessfully attempts to login via the authentication system a predetermined number of times, such as three times for example, than the authentication system locks the account and sends an email to the email address informing the user of the unsuccessful login attempts. The email might also include one or more pictures of the person who failed to login and GPS or other data from the login attempt. The user may then confirm whether this was a valid login attempt and reset the system, or the user may report the login attempt as fraudulent. If there is a reported fraudulent login, or if there are too many lockouts, the system may delete the account associated with the email address to protect the user's security. Thus, future fraudulent attempts could not be possible.

Feedback Meters

To further facilitate imaging, the mobile device may include various feedback meters such as a movement meter or accuracy meter as shown in FIG. 10 . In one embodiment, the mobile device 1012 may display a movement meter 1024 that indicates the amount of movement the mobile device 1012 makes as the user moves the mobile device 1012 to different positions relative to his/her head. For example, the movement meter 1024 may be represented as a line that slides from one side of the screen. In this manner, the enrollment process may require a certain threshold of device movement in order to register a user with the multi-dimensional authentication system. For example, the system could require that the mobile device 1012 is moved in an arc or straight line and rotate at least 45 degrees in order to create the enrollment information. In another example, the system could require an acceleration experienced by the device exceeding a threshold amount. The movement meter may also aid the user in learning how to image himself/herself using the authentication system.

The mobile device 1012 may also display an accuracy meter 1026 or any other visual representation of authenticated frames to aid the user in authenticating himself/herself using the authentication system and learning to improve authentication. The accuracy meter 1026 may show a user a match rate (graphical, alpha, or numerical) of a predetermined number of images obtained during the authentication process. The accuracy meter can be represented on the display in a variety of ways including numeric percentages, color representation, graphical, and the like. A combination of representations may also be utilized.

For example, as shown in FIG. 10 , match rates for a predetermined number of images taken during authentication are represented on the accuracy meter. In the embodiment shown in FIG. 10 , each of the images may be represented by a column in a graph, and the accuracy can be shown for each image in each column. For example, the column with a longer bar represent higher accuracy, and a column with a lower bar represents lower accuracy. In addition to match rates for images, the match rates for the path parameter may also be displayed. Over time the user can improve.

In another embodiment, each of the images may be represented on a table as a color that corresponds to the match rate. The color dark green may represent a very high match rate, light green may represent a good match rate, yellow may represent a satisfactory match rate, red may represent a mediocre match rate, and grey may represent a poor match rate. Other colors schemes may also be used.

The height of the bars or the colors used may correspond to predetermined match rates. For example, a full bar or dark green may be a match rate greater than 99.9%, a three-quarter bar or light green may be a match rate between 90% and 99.9%, a half bar or yellow may be a match rate of 50-90%, red may be a match rate of 20%-50%, and a single line to a quarter bar or grey may be a match rate of 0-20%. A pie chart, line graph, or any other type of representation could also be used or any other numerical or graphical display. An overall score may be presented or a score per image.

The accuracy meter may also include a message 1028 indicating an overall match score. For example, the accuracy meter may indicate an average overall match score or the number of images which achieved a 99.9% match rate, and display the message to a user. With the movement meter 1024 and the accuracy meter 1026 as described above, the user may quickly learn to use the authentication system due to the feedback presented by the meters 1024, 1026.

Gamification and Rewards

The movement and accuracy meters 1024, 1026 may also be configured to incorporates game features, aspects, or techniques into the authentication system to encourage a user to try and get the best match possible (such as a high number score or a high percentage of frames), increasing the user's skill in utilizing the authentication system. This also builds user adoption rates for the technology.

For example, the user may compete with themselves to mimic or improve past authentication scores to encourage or train the user to achieve a high score. Further modifications of the authentication meter may also be incorporated such as the ability to share accuracy match results with others to demonstrate one's skill in using the system or to compete against others. In other instances the user may receive a reward, such as a gift or coupon, for high accuracy scores. While this may slightly increase costs, the reduction in fraud loss would far outweigh the additional cost.

Further game techniques may be incorporated into the authentication system to encourage users to take actions which will prevent unauthorized or fraudulent authentication. In one embodiment, the authentication system may award users that engage in fraud preventing activities. One such activity is utilizing the facial recognition authentication system described herein. For example, based on the above described accuracy meter, the system may reward a user that successfully authenticates with the system above a certain match rate. The system may award reward points, cash, or other prizes based on the successful authentication or on a predetermined number of successful authentications. Where reward points are utilized, the points may be cashed in for predetermined prizes.

Other game features may involve award levels for users who gain a predetermined amount of experience using the authentication feature. For example, different reward levels may be based on users successfully authenticating 100 times, 500 times, 1000 times, etc. Because each instance of fraud loss can be significant and can damage the goodwill of the business or organization, the benefits to fraud prevention are significant.

In one embodiment, the user may be notified that he or she has achieved various competency levels, such as a “silver level” upon achieving 100 successful authentications, a “gold level” for achieving 500 successful authentications, or a “platinum level” for achieving 1000 successful authentications. A number of points awarded for each authentication above a given match rate may increase based on the user's experience level. Of course, the names of the levels and the number of authentications for each level as described above are only exemplary and may vary as desired.

In one embodiment, an authentication only counts toward reward levels when business is transacted at the web site while in other embodiments, repeated attempts may be made, all of which count toward rewards. Another feature may incorporate a leaderboard where a user may be notified of a user ranking comparing his or her proficiency or willingness in using the authentication system as compared with other users.

Successful use of the authentication system benefits companies and organizations that utilize the system by reducing costs for fraudulent activities and the costs of preventing fraudulent activities. Those cost savings may be utilized to fund the above described game features of the authentication system.

Further activities that correspond to the authentication system and contribute to the reduction of fraud may also be incorporated to allow a user to earn points or receive prizes. Such activities may include a user creating a sufficiently long and strong password that uses a certain number and combination of characters. This encourages and rewards users to set passwords that are not easily compromised. Other examples may include rewarding users to take time to perform verification steps in addition to an initial authentication such as a mobile phone or email verification of the authentication, answering one or more personal questions, or other secondary verifications as currently known or later developed. This rewards users for taking on added time and inconvenience to lower the risk of fraud to a company or organization.

As another example, if the authentication service is used to login to websites or apps that provide affiliate programs, then the reward or gift can be subsidized from the affiliate commissions on purchases made on those sites. For example, if a commerce (product or service) web site utilizes the method and apparatus disclosed herein to avoid fraud, and thus increase profits, then a percentage of each purchase made by a user using the authentication service will be provided to the authentication service. By reducing fraud, consumer purchases are more likely and additional users will be willing to enter financial and personal information. An affiliate link, code, or referral source or identifier may be used to credit the authentication system with directing the consumer to the commerce (product or service) web site.

Multiple Account Login

It is also contemplated that the authentication system may be configured to allow a user to access a number of different web sites as a result of a single authentication. Because the authentication process and result is unique to the user, the user may first designate which participating web sites the user elects to log into and then after selecting which one or more web sites to log into, the user performs the authentication described herein. If the secure authentication is successful, then the user is logged into the selected web sites. In this way, the authentication process is a universal access control for multiple different web sites and prevents the user from having to remember multiple different user names and passwords while also reducing fraud and password overhead for each user.

Automatic Start/Stop of Imaging

It is also contemplated that the system may be configured to have the video camera running on the phone. The mobile device would grab frames and path parameter data when the phone moves (using the camera, gyroscope, magnetometer, and accelerometer) but only process into biometric data on the device or send the frames up to the server if they have a face in them. In this embodiment, the application executing on the mobile device could trigger the software application to start saving frames once the phone is moving and then if the phone continues to move in the correct path (a semi-circle, for example) and the system detects a face in the frame the mobile device would start to send images, a portion of the image, or biometric data to the server for processing. When the system senses motion it may trigger the capture of images at certain intervals. The application may then process the frames to determine if the images contain a face. If the images do include a face then the application crops it out and then verifies if the motion path of the mobile device is similar to the one use used during enrollment. If the motion path is sufficiently similar, then the application can send the frames one at a time to the server to be scanned or processed as described above.

Banding and Edge Detection

When a fraudulent attempt is made using a display screen, such as an LED, LCD, or other screen, the system may detect the fraudulent login attempt based on expected attributes of the screen. In one embodiment, the authentication system will run checks for banding produced by digital screens. When banding is detected, the system may recognize a fraudulent attempt at a login. In another embodiment, the system will run checks for edge detection of digital screens. As the mobile device is moved to obtain the authentication movement during a login attempt, the system checks the captured images to for edges of a screen to recognize a fraudulent login attempt. The system may also check for other image artifacts resulting from a screen such as glare detection. Any now know or later developed algorithms for banding and screen edge detection may be utilized. Upon detection of fraud will prevent authentication and access to the website or prevent the transaction or account access.

Other Attributes Estimation

The authentication system may further conduct an analysis on the enrollment images to estimate at least one of a gender, an approximate age, and an ethnicity. In an alternative embodiment, the user may manually enter one or more of their gender, an approximate age, and an ethnicity, or this information may be taken or obtained from existing records which are known to be accurate. The authentication system may then further store a user's estimated gender, age, and ethnicity as enrollment credentials or user data. Thus when the user later attempts to authenticate with the system, the system will compare derived gender, age, and ethnicity obtained from authentication images (using biometric analysis to determine such data or estimates thereof based on processing) with the stored gender, age, and ethnicity to determine whether or not to authenticate the user. For example, if the derived data for gender, age and ethnicity matches the stored enrollment credentials, then the authentication is successful or this aspect of the authentication is successful.

The authentication system may make the gender, age, and ethnicity estimations based on a single image during the authentication process or based on multiple images. For example, the authentication system may use an image from the plurality of images that has an optimal viewing angle of the user's face for the analysis. In other embodiments, a different image may be used for each analysis of age, gender, and ethnicity when different images reveal the best data for the analysis. The authentication may also estimate the gender, age, and ethnicity in a plurality of the images and average the results to obtain overall scores for a gender, age, and ethnicity.

As an alternative to obtaining the gender, age, and ethnicity as enrollment information, the estimated gender, age, and ethnicity estimations as authentication credentials may be set over a course of repeated use of the authentication system. For example, if in previous successful authentications using biometrics and movement information, the authentication system always estimates a user's age being between 40 and 50, then the authentication may set credentials for that user requiring later login information to include images of a face estimated to be between 40 and 50. Alternatively, gender, age, and ethnicity estimations may be implemented as one of many factors contributing to an overall authentication score to determine whether or not to authenticate a user.

For example if the authentication process has a gender estimation of + or −0.2 of 1.9 male rating, then if the actual results do not fall within that range the system may deny access for the user. Likewise, if the user's age range always falls between 40-50 years of age during prior authentication attempts or enrollment, and an authentication attempt falls outside that range, the system may deny access or use the result as a compounding factor to deny access.

In a further embodiment, when a bracelet or watch capable of obtaining an EKG signature is used, a certain EKG signature may be required at login. The EKG signature could also be paired with the facial recognition rotation to provide multiple stage sign-on for critical security and identification applications. Further, the credentials could also include GPS information where login is only allowed within certain geographic locations as defined during enrollment. In one configuration the GPS coordinates of the mobile device are recorded and logged for a login attempt or actual login. This is additional information regarding the location of the user. For example, if the GPS coordinates are in a foreign country known for fraud, then the attempt was likely fraudulent, but if the GPS coordinate indicate the attempt or login was made in the user's house, then fraud is less likely. In addition some applications may only allow a user to login when at specified location such as a secure government facility or at a hospital.

The enrollment information may further include distance information. Because the motion arc (speed, angle, duration . . . ) is unique to each user, face detection software on the device can process the images and determine if the device is too close or too far from the subject. Or in other words, the enrollment information may take into account the size of the face in the images. Thus the potential enrollment information may also vary based on the length of a user's arm, head, and face size, and on the optics of the camera in the user's particular mobile device. The user may also be positioned at a fixed computer or camera, such as laptop, desktop, or atm. The user may then move the face either forwards and back, side to side, or up and down (or a combination) to create the images. Hence, this method of operation is not limited to a mobile device. In one embodiment, the camera is located in an automobile, such as in a mirror, and the person moves their head or face to authenticate.

Gradual Authentication Access

In one embodiment, the system is set to limit what the user can do when first enrolled and authenticated. Then, after further authentications or after a predetermined time period and number of authentications, additional capabilities may be granted. For example, during the first 20 authentications during the first 3 months, a maximum transaction of $100 may be allowed. This builds of database of known authentication data in connection with non-objected to transactions by the user. Then, during the next 20 authentications a transaction limit of $3000 may be established. This limits the total loss in the event of fraud when the authentication data is limited and the user is new to the system, for example if an unauthorized user is able to fraudulently enroll in the authentication system.

Video Display for Imaging

When the user images himself/herself using a front-facing camera, the user may confirm that his/her face is being imaged by viewing the image on the display, as described above. The image shown on the display may be configured so as to be smaller in area than the entire display, and may be positioned in an upper portion of the display towards the top of the device. When the user's image is shown only in the top portion of the user's display screen, the user's eyes tend to look more closely at the front camera. When the user's eyes are tracking up, the accuracy of the facial recognition may be improved. Further, tracking the movement of the eyes from frame to frame may allow the system to validate that the images are of a live person, and are not from a photograph or video recording of the person.

The image shown on the display may also be positioned to correspond with a camera location on the user's device, as shown in FIGS. 11A-11C. Mobile devices that are available today may include front-facing cameras disposed at a number of different positions. For example, one mobile device 1112 a, 1112 b may have a front-facing camera 1114 a, 1114 b that is disposed above the display and off center towards one side or the other, as shown in FIGS. 11A and 11B. Accordingly, the feedback image 1116 a, 1116 b of the user shown on the display may be positioned so as to correspond with the location of the camera 1114 a, 1114 b as shown. In FIG. 11A, where a camera 1114 a is above the display and is off-center at a position left of the center, then the image 1116 a may be shown in an upper left corner of the display. In FIG. 11B, where a camera 1114 b is above the display and is off-center at a position right of the center, then the image 1116 b may be shown in an upper right corner of the display. As shown in FIG. 11C, a mobile device 1112 c may have a camera 1114 c that is disposed centered directly above the display. There, the image 1116 c may be displayed centered in an upper portion of the display. In this manner, a user's eyes are directed close to and/or track as close to the camera as possible, aiding eye tracking and movement verification. The user is also able to better see the feedback image, and other feedback or information on the screen, as they move the mobile device.

The image viewed on the display by the user may further be modified such that the edge pixels on the sides display are stretched horizontally as shown in FIG. 12 . That is, a predetermined area 1206, 1208 on both the right and the left sides are warped to stretch towards right and left edges, respectively, of the screen. This allows a larger vertical portion of the displayed image to be shown on the display. Simultaneously, this trains a user to use the system correctly by keeping his or her face in the center of the screen, as his or her face would become warped on the screen if it becomes off center and part of the face enters the one of the warped areas.

Authentication in Low-Light Environments

To facilitate imaging, the screen on the mobile device may additionally be displayed with a white background, and the brightness of the screen may be increased to light up the user's face in dark environment. For example, a portion of the display could provide video feedback for the user to ensure he or she is imaging himself or herself, while the remaining portion of the display is configured to display a bright white color. Referring back to the example shown in FIG. 11C, this may be done by showing the video feedback 1116 c on a center of the display, with the surrounding areas being displayed as bright white bars around the video feedback 1116 c. In very dark situation, an LED flash on the back side of the mobile device and the back facing camera may be used. Alternatively, the camera may be configured to create an image using infrared light or other night vision techniques.

When infrared imaging is used as thermal imaging, further security enhancements are possible. Particularly, the thermal imaging may be analyzed to indicate whether or not the obtained images are from an actual user or are fraudulent images from a screen or other device. When a person is in front of an infrared thermal imaging camera, the heat radiation detected should be fairly oval shaped designating the person's head. In contrast, the heat radiating from a screen is typically rectangular. Further, the heat patterns detected in the actual person's face as well as the movement of the heat patterns in the images can be compared with expected heat patterns of a human face so as to distinguish the images from fraudulent authorization attempts using a screen.

Detecting Output from the Mobile Device

The display or other light source on the mobile device may further be utilized to provide additional security measures. During the authentication process described above, light from the display or other light source is projected onto the user's face and eyes. This projected light may then be detected by the camera of the mobile device during imaging. For example, the color tone detected on the skin, or a reflection of the light off of the cornea of a user's eye may be imaged by the camera on the mobile phone. Because of this, random light patterns, colors, and designs may be utilized to offer further security and ensure there is a live person attempting authentication and not merely an image or video of a person being imaged by a fraudster.

As one example, when a user begins authentication, the authentication server may generate and send instructions to the user's device to display a random sequence of colors at random intervals. The authentication server stores the randomly generated sequence for later comparison with the authentication information received from the mobile device. During authentication imaging, the colors displayed by the device are projected onto the user's face, and are reflected off of the user's eyes (the cornea of the eyes) or any other surface that receives and reflects the light from the screen. The camera on the user's mobile device detects the colors that are reflected off of the user's skin or eyes (or other surface) and generates color data indicating the colors detected based on the screen projection. This data may be returned to the authentication server to determine if the color sequence or pattern sent to the mobile device matches that known sequence or pattern projected by the screen of the user device. Based on this comparison at the authentication server the authentication is a success or denied. The comparison with the random sequence of colors in the instructions may alternatively occur exclusively at the user device to determine that a live user is being authenticated.

As another example, when a user begins authentication, the authentication server may send instructions the user's device to display a randomly generated pattern which is then stored on the authentication server. This pattern may include graphics, text, lines or bars, flashing light patters, colors, a QR code, or the like. The randomly generated pattern is displayed during authentication imaging, and the pattern is reflected off of the user's eyes (cornea). The camera of the user's device detects the reflected pattern off of the eye of the user and processes the reflected, mirrored image of the displayed pattern. The processed pattern (such as being converted to a numeric value) is transmitted to the authentication server and compared to the pattern that was randomly generated and stored on the authentication server to verify if the pattern displayed by the screen, and imaged after reflection off the user's face establishes a pattern match.

If a match occurs, this establishes or increases the likelihood that a live person is being imaged by the device. If the pattern is not a match, or does not meet a match threshold level, then the authentication process may fail (access denied) or the account access or transaction amount may be limited. It is noted that this example could also be incorporated on desktop computer with a webcam that does not incorporate the enrollment movement and authentication movement described above. Further, this example may not only be incorporated with facial recognition, but could also serve as an added layer of security for iris recognition or any other type of eye blood vessel recognition, or any facial feature that is unique to a user.

When the above example is implemented on a desktop computer, eye tracking may also be utilized to further demonstrate the presence of a live user. For example, the screen could show a ball or other random object or symbol moving in a random pattern that the user watches with his or her eyes. The camera can detect this real time movement to verify the user is live, and not a picture or display, and verify that the eye or head movements correspond to and match the expected movement of the object or words on the screen, which are known by the authentication system. Eye tracking can also be done by establishing an anchor point, such as via a mouse click at a location on the screen (assuming that the user is looking at the location where the mouse click takes place), and then estimating where the user is looking at the screen relative to the anchor position.

The use of a moving object on the screen may also be beneficial during enrollment on either a mobile or stationary device. For example, while capturing the enrollment images, the device may display a moving digital object (such as a circle or words(s)) that moves around the screen so that the user is encouraged to follow it with his or her head and eyes. This movement may be involuntary from the user, or the device may be configured to instruct the user to follow the object. This results in movement of the head and/or eyes creating small changes in the orientation of the user's head and face with the device camera, providing more complete enrollment information. With more complete enrollment information, the system may better ensure that the user will later be authenticated at a high rate even at slightly different angles during future authentication attempts.

Intuitive User Training and Enhanced Security by “Zooming”

In one embodiment, the system is configured to aid the user to easily learn to authenticate with the system. As shown in FIG. 13A, once enrollment or authentication is begun as described previously, the system causes the user's mobile device 1310 to display a small oval 1320 on the screen 1315 while the mobile device 1310 is imaging the user. Instructions 1325 displayed on the screen 1315 instruct the user to hold the mobile device 1310 so that his or her face or head appears within in the oval 1320. Because the oval 1320 is small, the user is required to hold the mobile device 1310 away from his or her body, such as by straightening his or her arm while holding the mobile device 1310. The maximum arm length and face size is unique to the user. In other embodiment, the arm may not be fully straightened such as to accommodate operation when space is not available, such as in a car or in a crowded location. It is noted that while the small oval 1320 is shown centered in the display, it may be positioned anywhere on the screen 1315.

Next, as shown in FIG. 13B, the system causes the user's mobile device 1310 to display a larger oval 1330 on the display 1315. The display 1315 may also show corresponding instructions 1335 directing the user to “zoom in” on his or her face in order to fill the oval 1330 with his or her face. The user does this by bringing the mobile device 1310 closer to his or her face in a generally straight line to the user's face (such as shown in FIGS. 7A and 7B) until the user's face fills the oval 1330 or exceeds the oval. In other embodiments, the large oval 1330 may simply be a prompt for the user to bring the mobile device 1310 closer to the user's face.

Thus, the system provides and teaches the user a simple method to provide enrollment and authentication images along with enrollment and authentication movement as explained above. The system may also teach varying enrollment and authentication movement by varying the location of the small oval 1320 on the screen 1315, and by changing the order and the size of the ovals displayed. For example the user may zoom in ½ way, then out, then in all the way, by moving the mobile device. The system may be configured to monitor that the camera's zoom function (when equipped) is not in use, which typically requires the user to touch the screen.

In one embodiment, the enrollment movement may be omitted, and the authentication movement may be compared to expected movement based on the prompts on the screen. For example, the device or authentication server generates a series of differently sized ovals within which the user must place his or her face by moving the mobile device held in the user's hand. In this manner, the authentication movement may be different during each login depending on the order, size, and placement of the ovals shown on the screen.

The system may also incorporate other security features when the “zoom in” movement is used as shown in FIGS. 13A and 13B. Typical cameras on a mobile device or any other device include a curved lens. This results in a “fish-eye” effect in the resulting images taken by the camera. In some instances, this curvature may not be visible to the human eye, or may only be noticeable at certain focal lengths. The curvature or fish eye effect can vary with focal length or distance between the user and the lens. The degree of the fish-eye effect is dependent on the type of optics used in the camera's lens and other factors.

The fish-eye effect becomes more pronounced on an image of a person's face when the person images his or her face close to the lens. The effect results in the relative dimensions of the person's face appearing different than when the imaging is done with the person's face farther away from the lens. For example, a person's nose may appear as much as 30% wider and 15% taller relative to a person's face when the image is taken at a close proximity as compared to when the image is taken at a distance. The differences in the relative dimensions are caused by the relatively larger differences in focal length of the various facial features when the person is imaged close to the lens as compared to the relatively equal distances in focal length when the person is imaged at a distance farther from the lens.

Such differences have been found to be significant in many facial recognition algorithms. That is, a facial recognition algorithm may not recognize a live person imaged at a close proximity and a far proximity as the same person. In contrast, if a two dimensional photograph of a person is imaged by the camera at both a close proximity and a farther proximity, the relative focal lengths between the lens and the two-dimensional image do not change so significantly. Thus, a facial recognition algorithm would recognize the two-dimensional photograph as the same person when imaged at both a close proximity and a distance farther from the lens.

This effect may be used to increase the security of the authentication system. For example, during enrollment, enrollment images may be provided by the user at both the close and far proximity from the lens, in addition to other positions through the movement. Later, during authentication, authentication images may be obtained at both the close and far distances from the lens to determine if they match with the enrollment information obtained from the enrollment images. Further, because the fish-eye effect is expected when an actual, three-dimensional person is present, an absence of the relative change in the dimensions of the facial features alerts the system to a fraudulent attempt at authentication. This effect could not easily be re-created with a two dimensional picture (printed photograph or screen) and thus, this step can serve as a secure test to prevent a two dimensional picture (in place of a live face) from being used for authentication.

In other words, using this movement of “zooming” in and out on the user's face, two or more biometric profiles could be created for the same person. One of the multiple profiles for the person may be imaged farther from the camera, and one of the multiple profiles may be for the person imaged closer to the camera. In order for the system to authenticate the person, the authentication images and biometrics must match the two or more profiles in the enrollment images and biometrics.

In addition, the system may detect the presence of a real person as compared with a fraudulent photograph of a person by comparing the background of the images obtained at a close and a far proximity. When the mobile device 1310 is held such that the person's face fits within the oval 1320, objects in the background that are almost directly behind the person may be visible. However, when the mobile device 1310 is held such that the person's face fits within the larger oval 1330, the person's face blocks the cameras ability to see the same objects that are almost directly behind the person. Thus, the system may compare the backgrounds of the images obtained at the close and the far proximity to determine whether the real person is attempting authentication with the system.

Of course, in FIGS. 13A and 13B, shapes or guides other than ovals 1320 and 1330 may be used to guide the user to hold the mobile device 1310 at the appropriate distance from his or her face. For example, the mobile device 1310 may show a full or partial square or rectangle frame. Further, the system may vary the size and location of the frame, such as the ovals 1320, 1330 to add further security. For example, the system may require a medium sized frame, a small frame, and then a large frame. As another example, the system may require a small frame at a first location and a second location, and then a large frame. This may be done randomly in order to teach different users different enrollment and authentication movements.

The number of frame sizes presented to the user may also vary for a single user based on the results of other security features described herein. For example, if the GPS coordinates of the mobile device show that the device is in an unexpected location, more frames at different distances may be required for authentication. One or more indicators, such as lights, words, or symbols may be presented on the screen so as to be visible to the user to direct the user to the desired distance that the mobile device should be from the user.

In FIGS. 13A and 13B, the system may predict the expected fish-eye distortion of the images based on the mobile device used for enrollment and authentication, and based on known and trusted enrollment data. In addition or as an alternative, the known specifications of a mobile phone camera for a given model may be utilized to predict the expected distortion of the person's facial features at different distances from the lens. Thus, the authentication may be device dependent. Further, enrollment information from the user is not required at every possible distance from the camera.

For example, as described above, enrollment images and biometrics may be obtained for a user at two distances from the user. During authentication, multiple images are captured in addition to images corresponding the close and far distances of the enrollment images and biometrics. Based on the expected distortion of these intermediary images according to the distanced traveled by the device, the system may validate that the change in distortion of the images is happening at the correct rate, even though only two enrollment profiles are obtained.

The capturing of these images may be still images or video, such that frames or images are extracted from the video that is taken during the movement from the first position distant from the user and the second position proximate the user. Thus, it is contemplated the operation may capture numerous frames during the zoom motion and ensure that the distortion is happening at the correct rate for the head size and the movement of the mobile device distance based on data from the accelerometers, magnetometers, and so forth.

Over time based on accumulated data, or calculated data during design phase, the system will have data indicating that if a phone is moved a certain distance toward a user's face, then the distortion effect should fall within a known percentage of the final distortion level or initial distortion level. Thus, to fool or deceive the authentication system disclosed herein, the fraud attempt would not only need to distort the fraudulent two-dimensional picture image, but would also need to cut the background, and then make a video of the face, distortion, and background that does all of this incrementally and at the correct speed, all while not having any banding from the video screen or having any screen edges visible, which is very unlikely.

Many currently known facial detection and facial recognition algorithms are configured to look for a small face within an image. Thus, in order to ensure that the facial detection and recognition algorithms detect and recognize the user's face in the zoomed in image (FIG. 13B), the system may add a large buffer zone around the image taken at a close proximity. This creates a larger overall image and allows current facial detection and recognition algorithms to detect and recognize the face, even where the face of the user is large in the original image.

When the enrollment and authentication movement resulting from the process described with FIGS. 13A and 13B is used, the eye tracking security features described above may also be enhanced. For example, when the user is instructed to bring the mobile device 1310 closer to his or her face to fill the oval 1330, the QR code, a random shape, a bar code, color, text, numbers or any other visual indictor may be displayed on the screen. At this close distance, the reflection of the displayed indicator off of the user's eye or face may be more easily imaged by the camera. Furthermore, eye movement, blinking, and the like to determine the “liveness” of the person being imaged may also be more easily obtained at the close proximity.

In one embodiment, at least one blink is required to prove liveness for authentication. In another embodiment, blinks may be counted and the number of blinks may be averaged over time during authentications. This allows for an additional factor in authentication to be the number of blinks observed during the motion. If a pattern of when the user blinks during the motion is observed, the system may verify that the user blinks at the expected time and device location during the motion during future authentication attempts.

In other embodiments, the size or location of the oval or frame may change to sizes or locations other than that shown in FIGS. 13A, 13B such that the user must position and/or angle the phone to place his or her face within the oval. This establishes yet another method of insuring liveness of the user.

In one exemplary method, the mobile device is positioned at a first distance from the user and a first image captured for processing. This distance may be linearly away from the user and in this embodiment not in an arc or orbit. This may occur by the user moving the mobile device, either by hand, or by the mobile device being on a movable device or rail system. Or, the lens system may be adjusted if in a fixed system to change the size of the user's face in relation to the frame size. Alternatively, the user may stay stationary, the multiple cameras may be used, or camera may move without the user moving. Once some form of movement (from a device, camera, lens, or user) has occurred to establish the camera at a second distance, a second image is captured for processing. Movement from the first position to the second position may be straight toward the user. Processing occurs on both images.

The processing may include calculations to verify a difference between the two images, or a difference in biometrics obtained from the two images, that indicates that a real person is being imaged. Processing may occur to compare the first authentication image to a first enrollment image (corresponding to the first distance) to determine if a match is present and then compare the second authentication image to a second enrollment image (corresponding to the second distance) to determine if a match is present. If a match occurs, then authentication may proceed.

Variations on these methods are also possible with the system requiring a match at the first distance, but a failure to match at the second distance, thereby indicating that the second image is not of a two-dimensional picture. The processing resulting in a match or failure to match may be any type image or facial recognition processing algorithm. As with other processing described herein, the processing may occur on the mobile device, one or more remote servers, or any combination of such devices.

All the processing described herein may occur on only the mobile device, only a remote server, or a combination there. The biometric data may be stored on the mobile device or the server, or split between the two for security purposes. For example the images could be processed on the mobile device, but compared to enrollment data in the cloud or at a remote server. Or, the images could be sent to the cloud (remote server) for processing and comparison.

Touch Screen Enhancements

Additional added security modifications may include information about a user's finger. Many mobile devices with touch screens can detect the location and approximate size of a user's touch on the screen. Accordingly, an approximate size of a user's finger or thumb may be measured by the system. In addition to the size of a finger, an orientation angle of the finger or whether the fingers or thumbs of the right or left hand are used can be detected.

In one embodiment, a user selects an account to open, begins enrollment imaging, or begins authentication imaging by touching the touchscreen of the user device. The authentication system may thus detect whether the touch by a user during authentication corresponds with previously stored enrollment information including the size of the user's finger or thumb, amount of pressure applied to the screen and whether the user is right or left handed. This adds an additional security layer for the authentication system.

Furthermore, the authentication system may require that the user initiates an authentication by touching a fingerprint reader or the touchscreen in one or more predetermined manners. In one embodiment, as shown in FIG. 14 , a touchscreen 1410 may be divided up into predetermined regions 1420. For example, there may be nine equal, circular, square, or other shaped regions 1420 on the touchscreen 1410 of the mobile device. During enrollment, the user selects one of the regions 1420 of the screen 1410 to touch to initiate authentication. During authentication, if the preselected region 1420 is not touched to begin authentication or during the entire authentication process, then authentication is denied. This is but one possible design possibility and other design options are contemplated.

The regions 1420 on the touchscreen may be visually represented by a grid, or may not be displayed at all on the touchscreen 1410. As shown in FIG. 15 , in addition to or in place of the regions 1420, buttons 1520 may be displayed on a touchscreen 1510. Here, the user may initiate the authentication by pressing one or more of the buttons 1520 in a predetermined pattern. The user may also initiate authentication via a predetermined swiped pattern. The position to be touched by the user may change with each authentication attempt and may be conveyed to the user through any instructions from the authentication server, such as a code, number, letter, color, captcha or other indicator.

Voice Parameters

It is also contemplated that the user could record their voice by speaking a phrase while recording their images during the enrollment process when first using the system. Then, to authenticate, the user would also have to also speak the phrase when also moving the mobile device to capture the image of their face. Thus, one additional path parameter may be the user's spoken voice and use of voice recognition as another layer or element of the authentication process.

Image Quality Assurance

The authentication system may also process the images received from the mobile device to determine if the images are of sufficient quality. For example, the system may check the images for blurriness caused by the images being out of focus or by the camera lens being obscured by fingerprints, oils, etc. The system may alert that user that the quality of the images is insufficient (or too bright or too dark) and direct the user to adjust a focus, exposure, or other parameter, or to clean the lens of the camera.

Autofocus

The authentication system may also utilize an autofocus feature when the mobile device camera is equipped with such. For example, when an actual, three-dimensional person is being imaged, the system checks to ensure that the sharpness of the image changes throughout as the camera perform auto-focusing. In another embodiment, the system may control the autofocus so that the camera focuses on a first location or distance to check for sharpness (in focus) of a portion of the image containing a face. The system then controls the camera to focus at a second location or distance where the presence of a face is not detected and check for sharpness (in focus) of a portion of the image. If a three dimensional person in a real environment is being imaged, it is expected that the focal length settings should be different at the first and second locations, which suggests a real person is presently being imaged. However, if the focal lengths of both locations are the same, this indicates that a two dimensional photograph or screen is being imaged, indicating a fraudulent login attempt.

The system may also control the auto-focus of the device to check for different focal lengths of different particular features in the image. For example, when a person's face is imaged from the front, a person's ear is expected to have a different focal length (more distant) than the tip of a person's nose.

Images of Login Attempt

The authentication server may also be configured to store the authentication images for a predetermined length of time. The images may provide additional security benefits as evidence of a person attempting to log in to a user's account. For example, the system may store a predetermined number of prior log in attempts, such as twenty login attempts, or store images from login attempts for a predetermined time period, such as during the past seven days or weeks. Any fraud or attempted fraud will result in pictures of the person attempting the login being stored or sent to the authentication server of the account server.

The mere knowledge that photos will be taken and sent is a significant deterrent to any potentially dishonest person because they know their picture will be taken and stored, and it is an assurance of security to the user. Likewise, any attempted and failed attempt can have the photo stored and indicator of who is attempting to access the account. It is also contemplated that an email or text message along with the picture of the person attempting the failed log in may be sent to the authorized user so they know who is attempting to access their account. This establishes the first line of security for the account as the user with the photo or image also being possessed by the authentication server.

Adaptive Match Thresholds

Further, the level or percentage of correspondence between the enrollment information and the authentication information to authenticate the user may change over time. In other words, the system may comprise an adaptive threshold.

After a user regularly uses the authentication system described above, the user will have logged in with the system by moving the mobile device in the predetermined path relative to his or her head a large number of times. Accordingly, it may be expected that as the user will gain experience using the authentication system, and that the user will gradually settle into a comfortable and standardized motion path. In contrast, the initial enrollment movement of a user will likely be the most awkward and clumsy movement as the user has little experience with the authentication system.

In order to make the authentication system more convenient for the user without losing security, the adaptive threshold system allow the enrollment movement to adapt so that the user is not locked into the awkward and clumsy initial movement as the enrollment movement. To facilitate this, upon each successfully authorization, the successful authorization movement is stored, and the motion path is added to a list of acceptable motion paths. The list of acceptable motion paths may be limited to a predetermined number of paths. When a new successfully authorization is completed and the list of acceptable motion paths is full, the older enrollment motion path is deleted and the newest is stored in its place. Alternatively, the motion path that is least similar to the other motion paths stored on the list may be deleted. Thus, by storing the most alike or newest motion paths, the enrollment movement may slowly adapt over time as the user because familiar with the system and settles into a comfortable motion path for authentication.

In addition, other enrollment information may adaptively change in a similar manner as the user information. For example, successful authentication photos or biometric information can be stored as part of the enrollment information, and old enrollment information may be discarded over time. In this manner, the authentication system can be convenient for a user even over a long period of time as the user experiences aging, facial hair growth, different styles of makeup, new glasses, or other subtle face alterations.

Determining how much variance is allowed over time in the motion path or the biometric information, or both may be set by the entity requiring authentication in order to meet that entity's security requirements. Time or number of scans after the initial enrollment can be used to modify the adaptive threshold. For example, during a first few days after enrollment, the threshold may be lower while a security threat is low and the differences in paths are likely to be higher. After a number of authentications or a number of days, the threshold may increase. The threshold further may be set based on trending data of either the motion path or biometric information. For example, the threshold may be more lenient in a direction the data is trending, while having a tighter tolerance for data against the trend.

A temporal aspect may also be added along with the location information. For example, if the user conducts and authenticates a transaction near his home, and then one hour later another transaction is attempted in a foreign country, the transaction may be denied. Or it may be denied if the distance between the prior authentication location and the next authentication location cannot be traveled or is unlikely to have been traveled in the amount of time between login or authentication attempts. For example, if the user authenticates in Denver, but an hour later an attempt is made in New York, Russia or Africa, then either first or second attempt is fraudulent because the user likely cannot travel between these locations in 1 hour.

Further, if the next transaction is attempted at a more reasonable time and distance away from the first transaction, the level of correspondence threshold may be raised to provide added security, without automatically denying the transaction. Likewise, an altimeter may be used such that if the altitude determined by the mobile device is different than the altitude of the city in which the user is reported to be located, then this may indicate a fraud attempt. Thus, altitude or barometric readings from the mobile device may be used to verify location and can be cross referenced against GPS data, IP address or router location data, or user identified location.

Random Image Distortion

In order to provide an additional layer of security to the facial recognition authentication system, the system may utilize random image distortion. For example, a user may be assigned a random distortion algorithm upon enrollment into the system. The distortion algorithm may include such distortions to the image as widening or narrowing the person's face by a predetermined amount, adding or superimposing a predetermined shape at a predetermined position on the user's face. As one example of this, the distortion may be a circle superimposed at 100 pixels above the user's left eye.

With the uniquely assigned distortion on the images from the user, the biometric data for that user will be unique to the account or device used by the user. That is, the enrollment biometrics stored on the authentication server or on the mobile device will reflect not only the facial features of the user, but also will reflect the uniquely assigned image distortion. Thus, even if an accurate, fraudulent representation of a person were used on a different device or via a different account, the proffered authentication biometrics would not sufficiently correspond due to a different or an absence of the unique distortion. Thus, the overall security may be enhanced.

Security Layers

It is noted that each of the above embodiments, modifications, and enhancements may be combined in any combination as necessary to create multiple layers of security for authentication. For example, the facial recognition may be combined with motion detection or path detection, or operate independently of these features for authentication. Further, when more than one of the above described enhancements or modifications are combined, the authentication system may be configured so as not to provide any feedback or indication on which layer failed authentication.

For example, when a predetermined touch pattern to initiate authentication is combined with the authentication movement and facial authentication, the system does not indicate whether a touch pattern was incorrect, or the authentication movement or authentication images failed to correspond to the enrollment information. Instead, the system provides an identical denial of authentication no matter what failure occurs. This is the case when any number of the security features described above are combined. In this manner, it is difficult for a fraudster to detect what aspect of the fraudulent credentials must be corrected, further enhancing the security of the system.

All of the above features may be incorporated together, or only some features may be used and others omitted. For example, when the device prompts the user to move the device so that the user places his or her head within a first small frame (such as an oval) then to a second large frame (such as in FIGS. 7A, 7B, 13A, and 13B), the system may be configured such that facial recognition need not be performed on the image(s) in the first frame (distantly captured frames). The security of the system is maintained by performing facial recognition throughout the imaging at some point between the first and second frames, and at the second frame. This may especially be true when also integrated another layer of security, such as checking eye tracking following a moving object on the screen, or reading a reflection of a QR code or random shape off of the user's eye. In another embodiment, when two or more cameras are used creating three dimensional, stereoscopic images, the facial recognition may not be performed at the first, far away frame, but instead the liveness of the person may be validated at the closer in frame only after the movement of the device. In still other embodiments, other security layers may be used, and the motion parameters may be omitted. Such combinations may be beneficial for larger or stationary devices, such as gaming laptop computers, personal desktop computers, a stationary kiosk, or the like.

Example Applications

Likewise, although described herein as financial account authentication, the authentication using path parameters and image data may be implemented in any environment requiring verification of the user's identity before allowing access, such as auto access, room access, computer access, web site or data access, phone use, computer use, package receipt, event access, ticketing, courtroom access, airport security, retail sales transaction, IoT access, or any other type of situation.

For example, an embodiment will be described where the above authentication system is used to securely conduct a retail sales transaction. In this embodiment, a user is enrolled with the authentication server or an authentication application on the mobile device as described above and has generated enrollment information including enrollment images and/or biometrics, and enrollment movement. In this example, the user initiates or attempts to complete a transaction at a retail establishment with a credit card, smart card, or using a smart phone with NFC capabilities.

The user begins the transaction by swiping a credit card, smart card, or using an application on a smartphone with NFC capabilities to pay for goods or services. The retail establishment would then authorize the card or account with the relevant network of the financial institution (“Gateway”). For example, the retail establishment, through a Gateway such as one operated by VISA or AMERICAN EXPRESS would determine whether the account is available and has sufficient available funds.

The Gateway would then communicate with the authorization server to authorize the transaction by verifying the identity of the user. For example, the Gateway may send an authorization request to the authentication server, and the authentication server then sends a notification, such as a push notification, to the user's mobile device to request that the user authenticate the transaction.

Upon receipt of the notification from the authentication server, such as through a vibration, beep, or other sound on the mobile device, the user may then authenticate his or her identify with the mobile device. The authentication server may also send information concerning the transaction to the user for verification by the user. For example, the authentication server may send information that causes the mobile device to display the merchant, merchant location, and the purchase total for the transaction.

Next, as before, the user may hold the mobile device and obtain a plurality of authentication images as the user moves the mobile device to different positions relative to the user's head. While moving the mobile device to obtain the authentication images, the mobile phone further tracks the path parameters (authentication movement) of the mobile device via the gyroscope, magnetometer, and the accelerometer to obtain the authentication movement of the device. The mobile device may then send the device information, the authentication images, and the authentication movement to the authentication server. In other embodiments, the mobile device may process the images to obtain biometric data and send the biometric data to the server. In still other embodiments, the mobile device may process the images, obtain the authentication information, compare the authentication information to enrollment information stored on the mobile device, and send pass/fail results of the comparison to the authentication server.

The authentication server may then authenticate the identity of the user and confirm that the user wishes to authorize the transaction on his or her account if the device information, authentication images and/or biometrics, and authentication movement correspond with the enrollment device information, the enrollment images and/or biometrics, and the enrollment movement. The authentication server then transmits an authorization message to the Gateway. Once the gateway has received confirmation of the authorization, the Gateway then communicates with the retail establishment to allow the retail transaction.

Several advantages may be obtained when a retail transaction is authorized utilizing the above system and method. Because the identity verification of the user and the confirmation of the transaction is completed via the authentication system and mobile device, there is no longer a requirement for a user to provide his or her credit card or signature, or to enter a pin number into the retailer's point of sale system. Further, the retail establishment does not need to check a photo identification of the user. The above method and system also has the advantage that it provides secure transactions that can work with mobile and online transactions that do not have cameras, such as security cameras, on the premises.

In the secure retail transaction described above, the user obtains the total amount due on his or her mobile device from the retail establishment via the Gateway and authentication server. However, in one embodiment, the mobile phone may use the camera as a bar code, QR code, or similar scanner to identify the items and the prices of the items being purchased. The mobile device may then total the amount due and act as the checkout to complete the transaction with the retail establishment.

In another embodiment, a user of the application may want to anonymously pay an individual or a merchant. In this instance, the user would designate an amount to be paid into an application, and the application would create a unique identifying transaction number. This number may then be shown to the second user, so the second user can type the identifying transaction number on an application on a separate device. The unique identifying transaction number may also be sent from the user to the second user via NFC, Bluetooth, a QR code, or other suitable methods. The second user may also type the amount and request payment.

Upon receiving the payment request and unique identifying transaction number, the authentication server may send a notification to the first user's mobile device to authenticate the transaction. The user would then verify his or her identity using the facial recognition authentication system described above. The user may alternatively or additionally verify his or her identity using other biometric data such as a fingerprint or retina scan, path based motion and imaging, or the user may enter a password. Upon authentication, the user's device would send a request to the user's payment provider to request and authorize payment to the second user. In this manner, the payment may be done securely while the users in the transaction are anonymous.

According to one embodiment, as an additional measure of security, the GPS information from the mobile device may also be sent to the authentication server to authenticate and allow the retail transaction. For example, the GPS coordinates from the mobile device may be compared with the coordinates of the retail establishment to confirm that the user is actually present in the retail establishment. In this manner, a criminal that has stolen a credit card and attempts to use the card from a distant location (as compared to the retail location) is unable to complete a transaction because the user's phone is not at the location of the retail establishment. IP addresses may also be used to determine location.

As explained above, the level or percentage of correspondence between the enrollment information and the authentication information to authenticate the user may also be adjusted based on the coordinates of the GPS of the mobile device. For example, if the retail establishment and GPS coordinates of the mobile device are near a user's home, then the level of correspondence may be set at a lower threshold, such as at a 99% match rate. Alternatively, if the location is very far from the user's home, and is in a foreign country, for example, then the level of correspondence may be set at a higher threshold, such as at a 99.999% match rate.

While various embodiments of the invention have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible that are within the scope of this invention. In addition, the various features, elements, and embodiments described herein may be claimed or combined in any combination or arrangement. 

What is claimed is:
 1. A method for enrolling and authenticating a user in an authentication system via a user's a mobile computing device comprising a camera and at least one movement detecting sensor, the method comprising: capturing enrollment biometric information from at least one first image of the user, the first image recorded by the camera of the mobile device; storing the enrollment biometric information on a memory; capturing authentication biometric information from at least one second image of the user, the second image recorded by the camera of the mobile device; recording, during imaging of the at least one second image, path parameter data from the at least one movement detecting sensor, the path parameter data representing an authentication movement of the mobile device; comparing the authentication biometric information to the stored enrollment biometric information to determine whether the authentication biometric information meets a predetermine similarity threshold as compared to the enrollment biometric information; comparing the authentication movement of the mobile device to an expected movement of the mobile device to determine whether the authentication movement meets a predetermine similarity threshold as compared to the expected movement; and when the authentication biometric information sufficiently corresponds with the enrollment biometric information, and when the authentication movement a predetermine similarity threshold as compared to with the expected movement, authenticating the user.
 2. The method according to claim 1, further comprising capturing, during imaging of the at least one first image, enrollment path parameters via the at least one movement detecting sensor, the enrollment path parameters indicating an enrollment movement of the mobile device, and defining the expected movement as the enrollment movement.
 3. The method according to claim 1, wherein the at least one first image and the at least one second image of the user include an image of the user's face, and the enrollment biometric information and the authentication biometric information comprise facial recognition information.
 4. The method according to claim 1, further comprising: capturing enrollment device information during imaging of the at least one first image; storing the enrollment device information on the memory; capturing authentication device information during imaging of the at least on second image; and comparing the authentication device information with the enrollment device information to determine whether the authentication device information sufficiently corresponds to the enrollment device information.
 5. The method according to claim 4, wherein the device information comprises at least one of the following: a device manufacturer, model number, serial number, and mobile network information.
 6. The method according to claim 1, wherein at least one movement detecting sensor comprises at least one of the following: an accelerometer, a magnetometer, and a gyroscope.
 7. A facial authentication system utilizing a user's mobile device, the mobile device comprising a camera and at least one movement detecting sensor, the authentication system comprising: an authentication server comprising a processor, a memory, and at least one transceiver configured to transmit data from the authentication server and received data; one or more memories configured to store one or more databases, the one or more databases configured to store authentication information profiles; and machine readable instructions that are stored on the memory of the authentication server which when executed by the processor, cause the authentication server to: receive enrollment biometrics from the mobile device, the enrollment biometrics comprising facial recognition data processed from at least one first image of the user's face obtained via the camera of the mobile device; store the enrollment biometrics in the one or more databases and associate the enrollment biometric data with the user authentication information profile; receive authentication biometrics from the mobile device during an authentication attempt by the user, the authentication biometrics comprising facial recognition data processed from at least one second image of the user's face, the image obtained from the camera of the mobile device; receive authentication movement information from the mobile device, the authentication movement information comprising data regarding movement of the mobile device during imaging of the at least one second image as detected by the at least one movement detecting sensor in the mobile device; compare the authentication biometrics and the authentication movement information with the enrollment biometrics and expected movement information stored in the database with the user authentication information profile; and authenticate or not authenticate the user based on a comparison match rate of the authentication biometrics and the authentication movement with the enrollment biometrics and the expected movement.
 8. The facial authentication system of claim 7, wherein: the mobile device displays a first prompt on a display screen to instruct the user to position a face of the user within a first display area of the display screen; the mobile device displays a second prompt on the display screen to instruct the user to position the face of the user within a second display area of the display screen, the second display area being a different size than the first display area; and the expected movement is based on an anticipated path of the mobile device to place the face sequentially in the first display area and the second display area.
 9. The facial authentication system of claim 7, wherein the authentication server detects the presence or absence of banding in the at least one second image.
 10. The facial authentication system of claim 7, wherein the authentication server detect the present or absence of a screen edge in the at least one second image.
 11. The facial authentication system of claim 7, wherein: the authentication server or mobile device estimates at least one of a gender, age, and ethnicity of a face imaged in the at least one first image; the authentication server estimates at least one of a gender, age, and ethnicity of the face imaged in the at least one second image; and the authentication server authenticates or does not authenticate the user based on a comparison match rate of at least one of a gender, age, and ethnicity detected in the at least one first image and the at least one second image in relation to a comparison match rate threshold.
 12. The facial authentication system of claim 7, wherein the comparison match rate threshold required for authentication is variable.
 13. The facial authentication system of claim 12, wherein the comparison match rate threshold increases based on a number of successful authentications.
 14. The facial authentication system of claim 12, wherein the comparison match rate threshold increases over time.
 15. A method for authenticating a user in an authentication system via a user's camera equipped computing device, the method comprising: capturing at least one first image of the user taken with the camera of the computing device at a first distance from the user; processing the at least one first image to obtain first biometric data based on facial recognition data from the at least one first image; capture at least one second image of the user taken with the camera of the computing device at a second distance from the user, the second distance being different from the first distance; processing the at least one second image to obtain second biometric data based on facial recognition data from the at least on second image; comparing the first biometric data to the second biometric data; when the first biometric data does not match the second biometric data within a predetermined threshold, authenticating the user; and when the first biometric data matches the second biometric data within a predetermined threshold, denying access to the user.
 16. The method according to claim 15, further comprising: capturing at least one first enrollment image of the user taken with the camera of the computing device at the first distance from the user; processing the at least one first enrollment image to obtain first enrollment biometric data based on facial recognition data from the at least one first enrollment image; comparing the first biometric data to the first enrollment biometric data; and authenticating the user when the first biometric data matches the first enrollment biometric data within a predetermined threshold.
 17. The method according to claim 16, further comprising: capturing at least one second enrollment image of the user taken with the camera of the computing device at the second distance from the user; processing the at least one second enrollment image to obtain second enrollment biometric data based on facial recognition data from the at least one second enrollment image; comparing the second biometric data to the second enrollment biometric data; and authenticating the user when the second biometric data matches the second enrollment biometric data within a predetermined threshold.
 18. The method according to claim 15, wherein the mobile device provides real time video feedback on a display of the mobile device during imaging.
 19. The method according to claim 15, further comprising distorting the at least one first image and the at least one second image prior to processing the at least one first image and the at least one second image.
 20. The method according to claim 15, further comprising displaying a pattern on the display of the mobile device during the imaging; and processing the at least one first image or the at least one second image to detect a reflection of the pattern off of an eye or face of the user. 